Distance to a point in a scene can be estimated from its location in two or more images captured simultaneously. The three dimensional (3D) position of the point can be computed from basic geometric relationships when the 3D relationship between the imagers is known. The challenge in computing distance from multiple images, often referred to as stereo correlation or stereo depth computation, is to automatically and accurately detect the mapping of a point in one image to its mapping in another image. This is most often done by correlating image features from one image to the other. This can be done in selected locations in the image (feature based stereo matching) or at each pixel (dense stereo matching). The underlying assumption in all stereo matching methods, however, is that there must be some identifiable local contrast or feature in the image in order to match that point to its location in another image. Therefore a problem arises when there is no local contrast or feature in the image because stereo matching does not produce valid results in portions of an image that correspond to surfaces with little texture. It would be beneficial if distance could be measured to all points in the image even those areas with no local contrast or features.